On Mar 18, 2009, at 10:11 , Ted Byers wrote:
On Wed, Mar 18, 2009 at 9:28 AM, Simon Urbanek
<simon.urba...@r-project.org> wrote:
Things cannot happen if you don't ask ...
Cheers,
Simon
Then I have two questions.
1) What multicore package? I didn't know there was one, and would
be interested in seeing what it does.
It's on CRAN, so just look at it for yourself:
http://cran.at.r-project.org/web/packages/multicore/index.html
multicore: Parallel processing of R code on machines with multiple
cores or CPUs
This package provides a way of running parallel computations in R on
machines with multiple cores or CPUs. Jobs can share the entire
initial workspace and it provides methods for results collection.
It's actually referenced in several places of the R website ...
2) Has there been any consideration of using Intel's threading
building blocks library?
I don't think so, because IMHO it makes no sense - you're missing the
main point that R is not thread safe. There are ways to use threads
from within R very cautiously (see Luke's parallelized vector math
operations for R for example). There are many good methods to use
threads in general (pthreads, OpenMP, GCD, ...) and you can do that as
long as you don't use memory allocation in R and don't call any R
functions that may do that (which is most of them ;)). Making R thread-
safe is not really an issue of a threading toolkit...
Cheers,
Simon
They do have an open source version. It can
apparently be built on Unix and Windows, and since it is open source,
you can adapt it to any platform that you need to support if there is
one that it doesn't presently support. And it allows a programmer to
work at a higher logical level than if he focussed largely on the
lower level details of creating and managing threads (for example, one
can create a 'parrallel for loop' and the library, once properly
initialized, will handle creating and cleaning up after any new
threads that may be needed): now that is a freedom in multithreaded
programming I truly appreciate. According to the documentation I have
read, it scales well to larger numbers of processors or cores. The
code in it that I have studied is all C++, so if there is much C++
code in R, it presents an option. I do know, though, that in my C++
code where it would provide a benefit (mostly vector and matrix
algebra), most of it would need to be completely rewritten. It does
present a rather different way of thinking about multithreaded
programming (it reminds me of the sort of thing I did when writing
code to run on a supercomputer supporting vector algebra decades ago).
With ITT, if Lapack was rewritten to take advantage of it, much of
the code would look quite different from what it does today. Of
course, if you're already using it, I might as well shut up and go
away. ;-) I am just learning to use it, as I am just learning to use
R, so I am afraid I can't offer much more info than this, though.
Cheers,
Ted
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